Papers Graph Matching
“Graph Matching” 태그가 달린 논문 515편 · 필터 해제
CURSOR: Scalable Mixed-Order Hypergraph Matching with CUR Decomposition
To achieve greater accuracy, hypergraph matching algorithms require exponential increases in computational resources. Recent kd-tree-based approximate nearest neighbor (ANN) methods, despite the sparsity of their compati…
Graph MatchingHypergraph MatchingTensor DecompositionMulti-graph Graph Matching for Coronary Artery Semantic Labeling
Coronary artery disease (CAD) stands as the leading cause of death worldwide, and invasive coronary angiography (ICA) remains the gold standard for assessing vascular anatomical information. However, deep learning-based …
AnatomyGraph MatchingPerfect Recovery for Random Geometric Graph Matching with Shallow Graph Neural Networks
We study the graph matching problem in the presence of vertex feature information using shallow graph neural networks. Specifically, given two graphs that are independent perturbations of a single random geometric graph …
Graph MatchingGraph Neural NetworkDo Vision and Language Encoders Represent the World Similarly?
Aligned text-image encoders such as CLIP have become the de facto model for vision-language tasks. Furthermore, modality-specific encoders achieve impressive performances in their respective domains. This raises a centra…
Graph Matchingimage-classificationImage ClassificationRetrieval+2Relational Matching for Weakly Semi-Supervised Oriented Object Detection
Oriented object detection has witnessed significant progress in recent years. However the impressive performance of oriented object detectors is at the huge cost of labor-intensive annotations and deteriorates once t…
Graph MatchingObjectobject-detectionObject Detection+2Mind Artist: Creating Artistic Snapshots with Human Thought
We introduce Mind Artist (MindArt) a novel and efficient neural decoding architecture to snap artistic photographs from our mind in a controllable manner. Recently progress has been made in image reconstruction with …
Graph MatchingImage ReconstructionRepresentation LearningUnsupervised 3D Structure Inference from Category-Specific Image Collections
Understanding 3D object structure from image collections of general object categories remains a long-standing challenge in computer vision. Due to the high relevance of image keypoints (e.g. for graph matching contro…
Graph MatchingObjectScene UnderstandingSAT-Based Algorithms for Regular Graph Pattern Matching
Graph matching is a fundamental problem in pattern recognition, with many applications such as software analysis and computational biology. One well-known type of graph matching problem is graph isomorphism, which consis…
Graph MatchingZeroReg: Zero-Shot Point Cloud Registration with Foundation Models
State-of-the-art 3D point cloud registration methods rely on labeled 3D datasets for training, which limits their practical applications in real-world scenarios and often hinders generalization to unseen scenes. Leveragi…
DecoderGraph MatchingObjectObject Localization+1xNeuSM: Explainable Neural Subgraph Matching with Graph Learnable Multi-hop Attention Networks
Subgraph matching is a challenging problem with a wide range of applications in database systems, biochemistry, and cognitive science. It involves determining whether a given query graph is present within a larger target…
Graph MatchingSpotServe: Serving Generative Large Language Models on Preemptible Instances
The high computational and memory requirements of generative large language models (LLMs) make it challenging to serve them cheaply. This paper aims to reduce the monetary cost for serving LLMs by leveraging preemptible …
GPUGraph MatchingTorchicTab: Semantic Table Annotation with Wikidata and Language Models
An abundance of tabular data exists and is used by a wide range of applications. However, a big portion of these data lack the semantic information necessary for users and machines to properly understand them. This lack …
Columns Property AnnotationColumn Type AnnotationGraph MatchingTable annotationExploring Naive Bayes Classifiers for Tabular Data to Knowledge Graph Matching
The present research investigates the use of Naive Bayes classifiers to match knowledge graphs and tabular data, with particular emphasis on Column Type Annotation, Cell Entity Annotation, Column Property Annotation and …
Cell Entity AnnotationColumns Property AnnotationColumn Type AnnotationGraph Matching+1GMTR: Graph Matching Transformers
Vision transformers (ViTs) have recently been used for visual matching beyond object detection and segmentation. However, the original grid dividing strategy of ViTs neglects the spatial information of the keypoints, lim…
Graph AttentionGraph Matchingobject-detectionObject DetectionBenchmarking PtO and PnO Methods in the Predictive Combinatorial Optimization Regime
Predictive combinatorial optimization, where the parameters of combinatorial optimization (CO) are unknown at the decision-making time, is the precise modeling of many real-world applications, including energy cost-aware…
BenchmarkingCombinatorial OptimizationDecision MakingGraph Matching+1OLaLa: Ontology Matching with Large Language Models
Ontology (and more generally: Knowledge Graph) Matching is a challenging task where information in natural language is one of the most important signals to process. With the rise of Large Language Models, it is possible …
Graph MatchingLanguage ModelingLanguage ModellingLarge Language Model+1Differentially Private Pre-Trained Model Fusion using Decentralized Federated Graph Matching
Model fusion is becoming a crucial component in the context of model-as-a-service scenarios, enabling the delivery of high-quality model services to local users. However, this approach introduces privacy risks and impose…
Graph MatchingPrivacy PreservingGraph Matching via convex relaxation to the simplex
This paper addresses the Graph Matching problem, which consists of finding the best possible alignment between two input graphs, and has many applications in computer vision, network deanonymization and protein alignment…
Graph MatchingM3C: A Framework towards Convergent, Flexible, and Unsupervised Learning of Mixture Graph Matching and Clustering
Existing graph matching methods typically assume that there are similar structures between graphs and they are matchable. However, these assumptions do not align with real-world applications. This work addresses a more r…
ClusteringGraph MatchingPseudo LabelUniMAP: Universal SMILES-Graph Representation Learning
Molecular representation learning is fundamental for many drug related applications. Most existing molecular pre-training models are limited in using single molecular modality, either SMILES or graph representation. To e…
Graph MatchingGraph Representation LearningMolecular Property Predictionmolecular representation+2